Societies, economies and ecosystems depend on sustainable and resilient hydrological services provided by in-tact hydrological systems and processes. These services are defined by the inter-dependencies of various drivers of which land use and land cover changes (LULC) as well as climate change are increasingly dominating, often leading to degraded hydrological services and altered ecosystem dynamics with different levels of societal and economic impacts.

Increasing and more frequent hydrological extremes (floods and droughts) and reduced water availability for various users and uses, often accompanied with increasing water demands, require effective operational water management. Therefore, in-depth knowledge of hydrological processes and their links to LULC, climate changes and other human interventions as well as tools are required to guide water management and policy decisions, such as, increasing water storage in grey and/or green infrastructure, caps on water consumption, or ecosystem restoration. This presentation reviews key processes and introduces the Hydrological Status and Outlook System (HydroSOS) and related assessment and reporting approaches to inform decision and policy-making. Therefore, a scale-specific approach is suggested in which LULC, water use and hydrological processes are embedded in a larger systems approach (including natural and human systems) to guide operational management and policies.

How to cite: Uhlenbrook, S., Mishra, S., Silva Vara, L. R., Korhonen, J., Otieno, W., and Kim, H.: Supporting operational water management and policy making through scale-specific approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17370, https://doi.org/10.5194/egusphere-egu23-17370, 2023.

Understanding the effects of climate change on surface-subsurface hydrology is critical for improving water resources management in a basin. In such cases, the use of hydrological models to quantify and assess water resources is a common practice. With the increasing population and human interventions, the land use changes drastically. The land cover plays a vital role in hydrology as it defines the properties of land surface in the models. So far, majority-of the studies accessing the future climate change consequences on hydrology take into account only the meteorological variables under different climatic projections, neglecting the future land use changes assuming it as static. However, that is not the case, because majority of the earth's surface has altered as a result of human activities, and these changes are represented in models via land use maps.
The study presented herein, aims to assess the surface-subsurface response of the catchment under combined effect of meteorological variables and land use future projection. The analysis is performed on the Aa of Weerijs catchment which is a meso-scale transboundary watershed between Belgium and the Netherlands. The future projections of the meteorological variable were obtained from the Royal Netherlands Meteorological Institute (KNMI-14) website for the Netherlands and the same trends were implemented for the Belgium part of the catchment. For the land use, the European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover (LC) maps of the study area for the year 1992 to 2021 were downloaded and linearly projected for the year 2050. The developed projected map was also compared with projected land use map of year 2050 by LUISA (Land Use-based Integrated Sustainability Assessment) modelling platform.
To investigate the hydrological regime of the area, the fully distributed physically based hydrological model coupled with a hydrodynamic model using MIKE-SHE and MIKE-11 modeling tools was developed. The base model was set up for the year 2009 to 2016. In addition to discharge, the groundwater heads are used to evaluate the model performance.
After setting up the base model, firstly, we analyzed the surface and subsurface response of the catchment considering that the land use in the area is the same as it was in 1992. Secondly, we analyzed the catchment response for the year 2050 by considering the meteorological variables as well as land use future projection.
The study provides unique estimates of future climate change and associated hydrological implications. The findings of the study will be valuable to plan and suggest significant modifications in the current strategies for water management in the area. Moreover, it can contribute to the efficient integration of spatial planning with water management.

How to cite: Ali, M. H., Popescu, I., Jonoski, A., and Solomatine, D.: Climate impact on surface-subsurface hydrology considering meteorological and land use projections., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2627, https://doi.org/10.5194/egusphere-egu23-2627, 2023.

Land cover in catchments undergoing shifting cultivation typically represents a mosaic of agricultural fields, fallows in different stages of regrowth, remnant forest, and degraded grasslands. Although runoff responses of the respective land-cover types are expected to differ, there is little quantitative information on how such a mosaic of land covers affects rainfall-runoff responses at the catchment scale.

From February 2015 to February 2016, we measured rainfall, streamflow, plus plot-scale (saturation) overland flow (SOF), soil water, and shallow groundwater dynamics under different land covers in the 31.7 ha Marolaona catchment in Eastern Madagascar. The catchment has undergone shifting agriculture for over 70 years which has resulted in a mosaic of vegetation at different stages of regrowth. Plot-scale hydrological responses varied between land covers and topographic positions, but catchment stormflow responses were generally small and dominated by pre-event water, suggesting that most storm-runoff was generated in the valley-bottom. However, for events exceeding an antecedent soil moisture storage plus rainfall threshold value, stormflow increased considerably, indicating contributions from the hillslopes as well. Despite lower rainfall in 2015/16, stormflow totals and annual water yield were higher than values reported for the driest years in the 1960s[1]. This is thought to reflect a deterioration of soil physical properties by repeated burning, cropping, and associated loss of topsoil during the intervening years, which has reduced the depth to an impeding layer and increases in amount and frequency of SOF.

To gain further insight into the runoff-responses across the catchment, we applied the RoGeR_Dyn model[2] using field-measured soil physical parameters for the respective land covers, topographic data, and climatic inputs. The simulations thus far highlighted greater deep percolation under tree fallows, while SOF was more common during the early stages of regrowth. Amounts of subsurface stormflow and percolation to deeper layers were highest in concave areas where flows converged. Soil moisture contents were lowest under tree-based land covers during the dry season. For nearly the entire dry season, rainfall events supplied the minimum amount of water needed to maintain soil moisture contents above critical levels for transpiration. This agrees with measured soil moisture and tree transpiration rates for nearby sites.

Although the hillslope modelling results are not expected to provide sufficient evidence to determine the effect of land cover on catchment-scale ecosystem services such as streamflow regulation, the lower runoff and higher deep percolation found along hillslopes under older fallows and forest suggest that these may be beneficial for flood mitigation and dry-season water provisioning to downstream areas. Next, RoGeR_Dyn will be used to determine the effects of regional changes in hillslope land cover and climate change on streamflow dynamics.

How to cite: Zwartendijk, B. W., van Meerveld, I., Teuling, A. J., Ghimire, C., Leistert, H., and Bruijnzeel, L. A.: Modelling the hydrological responses of a headwater catchment under shifting cultivation in upland Eastern Madagascar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8987, https://doi.org/10.5194/egusphere-egu23-8987, 2023.

Modelling and mapping water-related ecosystem services (ES) are getting more important in the scientific community and decision making contexts. Especially straightforward, easy to operate ES model suites that are based on Land Use / Land Cover (LULC) data have gained popularity in the scientific realm, including but not limited to the InVEST Model Suits. Model sensitivity to input factors has been widely assessed. However, little attention has been given to the effect on modelled ES distribution by user decisions such as the selection of LULC dataset. These crucial input data influence model results and hence, validity and credibility of model outputs and maps. Yet, many model applications aim to support policy and decision making, without properly specifying uncertainties of their modelling and underlying data.

Therefore, we investigated how to select appropriate and representative LULC data for model application. To test the effects of input LULC data on modelling results, we modelled the three regulating ecosystem services of water erosion control, water quality and water flow retention using InVEST. Different input LULC datasets were used to analyse how these datasets affect the modelling and mapping of ES supply. Taking a case study on Terceira Island, the Azores (Portugal), 3 LULC datasets were applied: (1) the EU-wide CORINE LULC (2018), (2) the Azores Region official LULC map (COS.A 2018) and (3) a remote sensing-based vegetation map using Sentinel-2 satellite imagery (2018). Output maps were compared by statistical analysis of class for distribution and similarity and visualized in similarity maps, showing the spatial variability between the three input LULC model results.

Model results show significant differences in distribution of water-related ES based on the different input LULCs. For the ES erosion control (Sediment Delivery Module), spatial distribution of modelled output maps differed greatly. Large homogenous agricultural areas in LULC datasets, in combination with steep slopes, present a skewed picture of erosion rates, simplifying the small patch structure with hedges and stone rows found on Terceira island. The modelling of Water quality, based on Nutrient Export Module, and flow retention, based on the Seasonal Water Yield Module, showed a more balanced and similar, yet significantly different spatial pattern of ES supply.

Therefore, we developed a guiding scheme to help researchers and practitioners select appropriate input LULC data for their ES modelling. Hereby, the availability of different LULC data is a first criterion. Factors such as LULC classes, especially linked to aquatic, riparian and agricultural land uses determine the level of detail of water-related ES modelling. Also, the scale of the assessment should be reflected in the average feature size and Minimum Mapping Units of the LULC dataset. Especially for local model applications, availability of high resolution LULC data, including structural elements, is preferred to obtain precise results.

How to cite: Sieber, I., Hinsch, M., Gil, A., and Burkhard, B.: Modelling water-related ecosystem services with InVEST - developing guidance on how to select appropriate land cover input data , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9882, https://doi.org/10.5194/egusphere-egu23-9882, 2023.

Interest continues to grow in the benefit of afforestation for carbon sequestration, yet the potential consequences of largescale afforestation on terrestrial hydrology are still unknown. Furthermore, it is unclear how large land cover changes may alter the surface-atmosphere hydrological connection, particularly as the climate and hydrological cycle evolve. In this study, we investigate how terrestrial and atmospheric hydrological processes in the UK may alter with increases in woodland across the UK, Ireland, and parts of Western Europe. We use a convection permitting physics-based regional climate model (HadREM3-RA11M) at 2.2 km resolution to simulate and identify the responses of afforestation on hydrology. Afforestation scenarios were generated using existing datasets from regional authorities and previous studies, with tree type determined according to pre-existing landcover. We compare modelled scenarios of widespread afforestation and existing land cover for a future period up to 2080 (with Representative Concentration Pathway 8.5) to assess the consequences of expanded woodland on terrestrial and atmospheric processes within the UK in a much warmer climate.

Model results show clear and substantial changes in hydrology in both the atmosphere and land surface with woodland expansion. Soil moisture increases, leading to a commensurate boost to subsurface flows, which is particularly greater in summer months. Although runoff increases throughout the country, there is a proportionally greater increase in the drier south-eastern UK. Evaporation broadly decreases across the country, primarily driven by a reduction in soil evaporation, although this varies seasonally. Precipitation patterns also alter substantially, with increases in the west and slight increases and decreases in the east of the country. These results provide unique insights into how models that couple the land surface with the atmosphere can identify potentially far-reaching consequences of afforestation in temperate regions.

How to cite: Buechel, M., Dadson, S., Slater, L., Berthou, S., Keat, W., and Lewis, H.: High resolution regional climate model points to a wetter UK with widespread afforestation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11158, https://doi.org/10.5194/egusphere-egu23-11158, 2023.

ReSET (Restarting Economy in Support of Environment, through Technology) is an EC H2020 research and development project focused on future and emerging technologies (FET) in Environmental Intelligence (EI). EI brings together multiple data streams, employing human reasoning and machine learning to better understand and manage the environment.

As part of ReSET, we are developing and deploying distributed networks of in-field sensors to monitor the hydrological impact of natural flood management, regenerative farming and other ecosystem restoration.  These sensor networks use FreeStation.org, low-cost, internet connected environmental sensing and data logging to provide locally specific evidence for the hydrological impact of a range of restoration investments in different locations and at different scales. More than 100 data loggers  have been deployed for 18 months collecting data every 10 minutes. 

This provides both capacity to directly analyse the effectiveness of investments in water ecosystem services  and co-benefits for non water ecosystem services and helps develop the understanding to better parameterise these restoration investments in spatial models like WaterWorld.  We apply  the WaterWorld Policy Support System  to assess the hydrological impact of novel scenarios for ecosystem restoration at the national and European scale. 

As well as ecosystem restoration, a  key  focus is regenerative agriculture (RA) which is a land management technique that involves no or low tillage, the use of cover crops and diverse crop rotations to help restore soil structure to a more natural state, encouraging infiltration and reducing runoff generation. This management technique has the potential to increase the water storage capacity of the soil, thereby reducing downstream runoff generation and flood risk..

Our local scale monitoring indicates that restoration of Eurasian Beaver habitat and of farmed soil through reduced tillage have the potential to increase flood storage locally and can reduce flood risk at downstream assets if applied at scale. Our national and continental scale modelling indicates that soil and canopy stores are critical to natural flood management since water body and wetland stores have only local influence and floodplain stores often contain important assets that preclude the use of the floodplain.  Ecosystem restoration has the potential to regenerate Europe's waters, but significant effort will be required to reach the level of restoration that will be needed 

How to cite: van Soesbergen, A., Burke, S., and Mulligan, M.: H2020ReSET: Monitoring and modelling the hydrological impact of ecosystem restoration scenarios at scales from local to continental using FreeStation and WaterWorld, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6801, https://doi.org/10.5194/egusphere-egu23-6801, 2023.

Hydrological models are crucial tools in planning the restoration of water-related ecosystem services because they help target priority areas for efficient resource allocation. However, when planning the expansion of Nature-based Solutions and Payments for Ecosystem Services, there are four key requirements that need to be taken into account. These are (1) the principle of additionality, which states that restoration policies must seek additional gains in terms of ecosystem services; (2) the representation of multiple runoff mechanisms, which can be fundamentally different in nature; (3) the calculation of farm-scale spatial outputs, which allows for the examination of the impacts of management practices at the level of individual farms; and (4) the estimation of epistemic uncertainty, which is the uncertainty that arises due to a lack of knowledge and information.

While addressing these requirements is important for making future planning more effective it can also be challenging. To address this challenge, this paper presents a comprehensive modeling framework that integrates these requirements in a way that allows for an improved selection of top priority areas with farm-scale spatial resolution, and a deeper understanding of how epistemic modeling uncertainty affects the results. This is particularly important when it comes to evaluating the risks of overestimating water-related ecosystem services benefits.

The modeling approach that we propose, called PLANS, uses the design of TOPMODEL to simulate both saturation-excess and infiltration-excess runoff at the farm-scale resolution. It also employs a novel saturation index based on a combination of the Height Above the Nearest Drainage (HAND) and Topographical Wetness Index (TWI) terrain descriptors. To estimate output epistemic uncertainty, we apply the Generalized Likelihood Uncertainty Estimation (GLUE) method, aided by an evolutionary algorithm. We demonstrate the effectiveness of the PLANS model in a case study watershed in the Atlantic Forest biome of Brazil. Our results show that uncertainty can significantly impact the definition of priorities, with a 97% ranking change. We also find that simulated topographic effects can outweigh local effects of land cover and soil type. By better evaluating uncertainty, we demonstrate that the cost of the restoration program in the study case could potentially be reduced by up to 27%, making it more cost-effective.

Overall, our modeling approach offers a promising way to address the challenges of planning the expansion of Nature-Based Solutions in watersheds and deploying programs of Payments for Ecosystem Services. It allows for the improved selection of top priority areas and a deeper understanding of the impacts of epistemic uncertainty on the outputs. By taking these considerations into account, society can make more informed decisions about how to allocate resources and design restoration programs that are both effective and efficient.

How to cite: Possantti, I., Barbedo, R., Kronbauer, M., Collischonn, W., and Marques, G.: Modeling priority areas for restoration of water-related ecosystem services under epistemic uncertainty: a case study in the Atlantic Forest, Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3393, https://doi.org/10.5194/egusphere-egu23-3393, 2023.

The limited spatial scope of Andean basins conservation agreements by nature-based solutions (NbS) in the tropical alpine grassland region (páramos) has led to unequal protection of upstream ecosystems, endangering the water quality downstream of large Andean cities. Simultaneously, the páramos have been converted to other land uses in response to the political, economic, social, technological, ecological, and legal (PESTEL) factors. In this context, we aim to compare the negative impacts associated with land use/land cover (LULC) change and the positive effects of nature-based solutions in the upper Andean basins using remote sensing data, PESTEL analysis, and water quality assessment. The upper basin of the Pita and Cutuchi rivers, located above 3,000 m.a.s.l., which supply drinking and irrigation water to two major Andean cities in Ecuador, i.e., Quito (the capital of Ecuador) and Latacunga (an important city for floriculture and agriculture), were examined in comparison as case studies between 1999 and 2022. Our results reveal significant land-use changes from páramo to agriculture, Pinus plantations, urban growth, and mining areas in the upper basin of the Cutuchi river, driving water quality between low to moderate for drinking and irrigation purposes. According to the PESTEL framework, the main factors contributing to the lack of upper basin protection are (1) short-term policies in line with the political party, (2) state budget planning that does not meet restoration needs, (3) conflicts between the upper, middle, and lower river basin communities, (4) lack of public investment in technological tools, (5) agricultural practices in the páramo due to high soil carbon storage in comparison to other areas and (6) conflicting laws between administrative divisions. In contrast, main páramo areas have remained unaltered or passively restored in the upper basin of the Pita river by the combination of NbS and policies implemented by water funds, conserving good water quality. By using these catchments as ideal natural laboratories, we can demonstrate the positive experiences through NbS in river basin management. Together with our PESTEL analyses, it is possible to develop integral conservation projects that ensure human health and sustainable agricultural productivity in the context of Sustainable Development Goal (SDG) 6: Clean Water and Sanitation.

How to cite: Fonseca, K., Ramírez, M., Martínez, W., Espitia, E., and Breuer, L.: Assessing the impacts of land use/land cover (LULC) change and the effects of nature-based solutions in Andean basins., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6035, https://doi.org/10.5194/egusphere-egu23-6035, 2023.

Land use changes can affect many dimensions of the hydrological cycle which in turn affect the provisioning of water and its related ecosystem services to society. Modification at different spatial and temporal extents due to seasonal changes in water supply and land use intensities may compound and challenge our ability to predict the cascade of processes that lead to the supply of ecosystem services, i.e., ecosystem service cascade (ecosystem property, supply and service). In the Amazon basin, land use changes may affect water supply through modification of moisture recycling periodicity, and a quantification of its effects on other water-related ecosystem services, namely crop production and biodiversity, is scarce. We investigated this process using a moisture-tracking model, to show that upstream land use changes will affect the persistence of cropland in the Amazon arch of deforestation.  We also show that biodiversity trait distributions affect the provision of water that maintains the cascades of moisture recycling, and different trait combinations enable regulation of atmospheric water regulation and land surface temperature. As trait combinations are a result of land use changes, the future of moisture recycling in the Amazon and its dependence downstream may require a better land use planning that incorporates these processes more explicitly.

How to cite: Santos, M. J., O'Connor, J. C., Nguyen, K., Tuinenburg, O., and Dekker, S. C.: Effects of land use change on water-related ecosystem services in the Amazon Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14983, https://doi.org/10.5194/egusphere-egu23-14983, 2023.